code: def standard_scale(train_inputs, non_train_inputs):
      scaler = StandardScaler()
      train_inputs[train_inputs.columns] = scaler.fit_transform(
          train_inputs.values
      )
      non_train_inputs[train_inputs.columns] = scaler.transform(
          non_train_inputs.values
      )
      return train_inputs, non_train_inputs
  
file_path: hyperparameter_hunter/tests/integration_tests/feature_engineering/test_support.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  from hyperparameter_hunter import Categorical, GBRT
  from hyperparameter_hunter.utils.learning_utils import get_boston_data
  import pytest
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import QuantileTransformer, StandardScaler
label_desc: Use image node_id 2 for calling a global function with example usage: standard_scale(train_inputs, non_train_inputs) and returns: train_inputs,  non_train_inputs